Picture this: April 8, 2026, a quiet terminal flickering in a home office as <a href="/tag/heka-insights-agent/">heka-insights-agent</a> hums to life, its first production-ready collectors snapping CPU stats without breaking a sweat.
This lean Python telemetry agent isn’t vaporware. Ronin1770 just wired up CPU, memory, and disk collectors into a shared polling loop — all in src/main.py, pulling intervals from a .env file via python-dotenv. We’re talking real code, pushed to GitHub, ready for the wild.
And here’s the thing — in an era drowning in 500MB observability suites, this agent’s profiling shows max RSS at 15MB, CPU near-idle, even on 5-second polls. Disk collection? Heaviest hitter, sure, but still a blip after caching physical devices.
Why Snapshots and Deltas Are the Secret Sauce
CPUCollector in src/collectors/cpu.py ditches the usual suspects. No cpu_percent() and cpu_times_percent() double-dip. Instead:
I built CPU collection around
<a href="/tag/psutil/">psutil</a>.cpu_times(...)snapshots and delta math (single source), instead of calling bothcpu_percentandcpu_times_percentper cycle.
Smart. First cycle warms up — by design. MonotonicTicker keeps cadence rock-steady, no drift from sleep slop. Basic mode? Compact. Detailed? Per-core deep dive, optional.
No thread offloading. Why? This workload’s synchronous bliss — psutil’s fast enough.
MemoryCollector? Even lighter. One virtual_memory() call, one swap_memory(). Basic spits key fields; detailed dumps the full psutil buffet. Bytes stay raw — server crunches the percentages.
Disk? Cumulative I/O counters via disk_io_counters(perdisk=True). Aggregate plus per-disk, physical devices only — partitions ignored. Device-name cache refreshes periodically, slashing filter overhead every cycle.
Post-profile tweaks sealed it: cached physical list for disk, output shape untouched.
How Did Profiling Flip the Script?
120-second run. cProfile lit up. Sleep dominates — as it should. Collectors? Pennies on the CPU dollar.
But disk was the outlier. Response? Cache that device list. Boom — optimized without bloat.
Main loop’s elegant: CPU, then memory, then disk, all ticking to CPU_POLL_INTERVAL_SECONDS (defaults 5s on bad env). Separate logs per collector. Clean.
Is This the Anti-Bloat Rebel Observability Needs?
Look, we’ve seen this before. Early Prometheus node_exporter — lean, unix-y, one job: scrape metrics. Nagios plugins, too. But today’s stacks? Datadog agents ballooning gigs, New Relic sipping resources like fine wine.
Here’s my unique bet: minimalist Python telemetry agents like heka-insights-agent will rule edge computing and IoT by 2028. Why? Cloud-native’s fatigued — Kubernetes clusters groan under agent sprawl. This? 15MB RSS. Ships to any backend. Extension points scream for network, retries, tests.
Ronin1770’s not hyping. No “revolutionary” fluff. Just a baseline pipeline with low overhead. Next: payload shipping, backoff, collector tests. Repo: github.com/ronin1770/heka-insights-agent. Fork it.
Skeptical? Profile yourself. Run it on a VM. Watch idling triumph over enterprise telemetry’s endless wars.
Corporate spin calls this “basic.” Nah. It’s architectural judo — flip overhead on its head by offloading smarts server-side.
Why Does a Shared Loop Beat Fancy Scheduling?
Wiring all three into one loop? Counterintuitive at first. CPU’s delta math needs precise ticks — memory and disk? Fire-and-forget lightweight.
But shared cadence wins. No scheduler overhead. MonotonicTicker ensures no drift across all. Poll interval env-driven — dev, prod, tweak without recompiles.
Fall back to 5s on invalid? Defensive. Real-world ready.
Disk’s per-disk filtering? Cached. No repeated lsblk or whatever psutil hides.
This isn’t toy code. Production-ready, says the log.
The Bigger Shift: Server-Side Wins in Telemetry
Raw bytes, cumulative counters — agent stays dumb, fast. Backend transforms. Scalable genius.
Per-core CPU optional. Detailed modes for when you need it. Basic for shipping fleets.
Profiling proved it: runtime’s 99% sleep. Collectors? Microseconds.
Will Lean Agents Kill the Giants?
Not kill. Outrun. In cost-sensitive clouds, IoT, homelabs — yes. Datadog et al charge per host, per GB. This? Free, lean, yours.
Historical parallel: tcpdump over Wireshark for prod. Minimal tools endure.
Ronin1770’s agent embodies that. Baseline for shipping essential Linux telemetry.
Grab the repo. Build on it.
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Frequently Asked Questions
What is heka-insights-agent?
Lightweight Python agent for Linux CPU, memory, disk telemetry — polls efficiently, ships raw to backends.
How to build Python telemetry agent with psutil?
Start with CPU snapshots/deltas, cache disk devices, shared monotonic loop — profile ruthlessly.
Does heka-insights-agent replace Prometheus exporter?
No, complements — leaner footprint, Python-first, easy extension for custom shipping.
